The phrase “Spectrum Handoff” or “Spectrum Handover” refers to the procedure used in the cognitive radio (CR) network for users to change spectrum bands. A transceiver can intelligently determine which communication channels are in use and which ones are not in CR, a form of wireless communication [1]. The transceiver then immediately switches to open channels, avoiding busy ones [2]. Moreover, it increases spectrum efficiency and the consumer’s quality of service (QoS) through avoiding occupied channels. With the explosive expansion of wireless communication industries [3], a significant demand exists for establishment of novel wireless networks in licensed and unlicensed frequency spectra. Recent research demonstrates that the current fixed spectral assignment approach leads to subpar spectrum utilization ^[4][5][6][4,5,6]. Cognitive radio networks (CRNs) have emerged as a viable technique to solve this issue by allowing access to the sporadic intervals of vacant frequency bands, often known as white space or spectrum gaps, and therefore improving spectrum efficiency (SE) ^[7][8][9][7,8,9]. In the most basic sense, every CR user in a CRN must first determine if licensed users, also known as primary users (PUs), are present and if not, whether the spectrum is accessible. Spectrum sensing (SS) is a kind of radio frequency (RF) environment sensing that is typically used to accomplish this ^[10][11][12][10,11,12].

SS has two goals: first, CR users must get out of interfering negatively with PUs by moving to an open band to a reasonable level ^[13][14][15][13,14,15]. Second, to attain the essential throughput and QoS, CR users should effectively locate and utilize the spectrum gaps ^[16][17][18][16,17,18]. Therefore, the effectiveness of primary and cognitive radio networks depends on the detection accuracy in SS ^[19][20][19,20].

The performance of detection could be determined primarily depending upon two metrics: false alarm (FA) probability indicates the probability of a CR user stating that a PU is available while the spectra are free, and detection probability indicates the probability of CR user portraying that a PU is available while the spectra are indeed engaged by a PU [21]. As a detection miss leads to intervention with PUs and a FA would lessen the SE, it is typically necessary for optimum detection performance where the probability of detection is increasingly subjected to an FA probability [22]. The performance of detection in SS may be considerably hampered by a variety of issues, including receiver uncertainty, shadowing, and multipath fading [23].

In 2018, Muthukkumar and Manimegalai [24] examined the collaboration between secondary users (SUs) and main users using the Priority-Based Two-Stage Detection Model (PBTSDM). SUs in distributed CSS continually sensed among themselves and used an entropy-based energy detection approach to jointly determine whether or not PUs were present. The outcomes displayed that applying the suggested technique considerably improved the accuracy of energy efficiency (EE) and sensing time. However, noise uncertainty was a concern.

In 2017, Atmaca et al. [25] used cooperative spectrum sensing to maximize the throughput of Carrier Sense Multiple Access (CSMA) in Random Access CRNs (RACRNs). A CRN was simulated using the CSMA media access control (MAC) system in this researchtudy, with a particular emphasis on examining its throughput performance. In the identical network-level condition, throughput performances of CRNs were achieved and compared. Nevertheless, the network load needed to be concentrated more.

In 2019, Sharifi [26] offered an effective protection strategy using the Attack Aware CSS (ACSS). The concept was based on the assessment of attack strength, where attack population and assault strength were correlated. The chance that a particular sensor was malicious is equal to the ratio of malevolent sensors to all sensors, which was known as the attack strength. The suggested method predicted attack strength and used the Bayesian hypothesis test to enhance collaborative sensing performance, supposing malicious sensor activity or an attack plan. However, strong interference might affect PUs.

In 2021, Ye and Jiang [27] proposed a study on cluster-based CRNs that included an ideal linear-scaled CSS. Different weight values for cooperative nodes were assigned in this system depending on the signal-to-noise ratio (SNR) of CR users and the historic sensing accuracy. Additionally, the CR users could be grouped, and the cluster heads chosen to collect the local sensing data were the users with superior channel characteristics. The suggested approach provided superior sensing performance while also increasing detection probability and lowering error probability, according to the simulation findings. More experimental platforms need to be considered to confirm the feasibility of this approach.

In 2021, Devi and Umamaheswari [28] included the use of the M/G/1 queuing model and the Spectrum Binary Particle Swarm Optimization (Spec BPSO) algorithm for the prediction of an efficient spectrum handoff method. Cluster-based CSS (CBCSS) was employed to increase SU effectiveness and decrease channel congestion. This research project also provided a framework for observing how main user behavior affected spectrum handoff performance delays with potential CRN interruptions. Nevertheless, metaheuristic schemes were not focused on.

In 2020, Rajaganapathi and Nathan [29] developed the accurate CSS and optimal relay selection (ORS) system, which enhanced the SUs using a hybrid CRN throughput. The precision of choosing the underlay/overlay technique to convey information was increased by an accurate CSS approach. When an underlying transmission strategy is chosen, SUs employ relays to reduce interference. An optimal relay selection approach was applied in this case to optimize relay choice. The throughput was improved by the suggested system, according to the numerical data. In the future, optimization concepts can be included to ensure more enhanced results.

To effectively use the report time slot by increasing the detecting time of SUs, in 2021, Hossain et al. [30] suggested the idea of Multiple Reporting Channels (MRCs) for clustered CRNs. In this method, each cluster was given a reporting channel for reporting purposes. The designated single reporting channel was used by all the SUs in every cluster to progressively transmit their sensing findings to the associated CH, extending the SUs’ sensing time length. This method considerably improved all SUs’ sensing times compared to non-sequential reporting and also reduced all cluster heads’ (CHs’) reporting time delays compared to sequential single-channel reporting. Multiple PUs as well as ML concepts were not taken into account.

In 2018, Jaglan et al. [31] deployed Artificial Neural Networks (ANNs) at fusion centers, which resulted in a notable improvement in detection accuracy and a decrease in the FA rate when compared to traditional methods. It was determined that the suggested ANN technique can handle CRN scalability while maintaining performance. Additionally, the SNR of each SU was taken into account while making decisions at the fusion center. Furthermore, the suggested method was evaluated for resilience against security attacks (malicious users) and unintentional mistakes happening at SUs. A minimal amount of FA issues occurred.

In 2022, Arshid et al. [32] deployed a user transmission system that senses available channels through cooperative spectrum sensing. Energy economy was achieved by optimizing the energy consumption of the sensing process. For spectrum managing, a threshold method based on main user traffic patterns was presented. A CSS was also explained and executed to find the best channel with the highest throughput and least amount of energy use. The suggested method improved throughput and energy efficiency while maintaining the handoff delay, and preventing false alarms and missed detection.

In 2022, Bani and Kulkarni [33] deployed a hybrid detector (HD) to identify spectrum holes using the available resources. An energy detector (ED) and matched detector (MD) served as the foundation for the HD architecture. The HD was able to sense the signal more accurately than a single detector like an ED. Whether or not the primary user information was accessible in this case, HD functioned under both circumstances. Under heterogeneous conditions, HD was analyzed both with and without spectrum sensing. The IEEE Wireless Regional Area Network (WRAN) 802.22 standard served as the foundation for the HD’s design specifications. OR rules produced the best outcomes for the HD model.